Beacon-based indoor positioning is popular in recent years. In this work, the authors aim to enhance the positioning accuracy by proposing signal power ranking (SPR) and solving related optimisation-based deployment p...
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Beacon-based indoor positioning is popular in recent years. In this work, the authors aim to enhance the positioning accuracy by proposing signal power ranking (SPR) and solving related optimisation-based deployment problem of beacons using wireless communication and Bluetooth 4.0 Bluetooth low-energy network technologies. The authors first adopt grid-based field to be the proposed deployment field. Second, they convert the received signal strength indicator (RSSI) to several levels called SPR. Third, an optimisation-based model for deployment problem of beacons in indoor positioning is proposed on the basis of the above two considerations. The proposed model is to minimise the number of beacons required under some fundamental conditions including full coverage and full discrimination, respectively. Finally, the algorithm of simulated annealing is applied to solve the linear programming problem in this model. By the optimal results, the user can obtain a vector table of RSSI for each location efficiently in the test field. On the other hand, the user in the test field can receive the beacon RSSI value at the same time. In order to determine the user's location, the received beacon RSSI value is compared with the values in the vector table.
A novel optimisation-based model of the power flow (PF) problem is proposed using complementarity conditions to properly represent generator bus voltage controls, including reactive power limits and voltage recovery p...
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A novel optimisation-based model of the power flow (PF) problem is proposed using complementarity conditions to properly represent generator bus voltage controls, including reactive power limits and voltage recovery processes. This model is then used to prove that the Newton-Raphson (NR) solution method for solving the PF problem is basically a step of the generalised reduced gradient algorithm applied to the proposed optimisation problem. To test the accuracy, flexibility and the numerical robustness of the proposed model, the IEEE 14-bus, 30-bus, 57-bus, 118-bus and 300-bus test systems and large real 1211-bus and 2975-bus systems are used, benchmarking the results of the proposed PF model against the standard NR method. It is shown that the proposed model yields adequate solutions, even in the case when the NR method fails to converge.
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